Repo Prompt's MCP Server Explained in 5 Minutes
The latest 1.3 update for Repo Prompt introduces a significant enhancement: the MCP server. This article explains what this new feature does and how it can streamline your development workflow.
While manually selecting files in Repo Prompt to build your context is still a powerful feature, the new MCP support takes it to another level. With tools like Claude Desktop, you can now programmatically interact with the application, letting it perform research and assist with file selection to construct your prompts more efficiently.
A Practical Use Case: Streamlining Documentation
Imagine a scenario where you have both a web application and a Swift app within the same workspace. This setup is particularly useful when working on documentation. To accurately document features, one must first understand the underlying code. The MCP server helps bridge this gap by providing a comprehensive understanding of the codebase, making it easier to explain the logic and functionality.
Let's see how this works in practice. To learn about the new MCP feature, we can instruct an AI assistant like Claude to analyze the relevant files. The process begins with an exploration phase, where the assistant reads through the codebase.
The Repo Prompt MCP exposes several powerful tools, some of which were previously available, such as the ability to read the file tree. This allows the AI to see the directory structure of selected files, access code maps, and utilize various other informational resources. The AI assistant will first examine the currently selected files and then read through them to gather necessary information. Its initial goal is to determine which files are related to the MCP services to build a foundational understanding.
The system leverages advanced search tools, similar to those found in dedicated IDEs, which can be integrated with any AI-powered application. This enables a sophisticated analysis of the code.
Automated Code Analysis
During the analysis, the AI explains the feature's setup in detail and identifies all accessible tools. It performs a comprehensive review of the repository, highlighting different features, identifying potential gaps, and providing a clear overview of the project's state.
This capability is incredibly powerful. The AI can read both the documentation and the feature's code, then synthesize that information to explain how it works. This provides actionable insights for developers.
Bridging Tools and Models
With numerous tools available, what else can be achieved? A significant advantage emerges for users of specific platforms who might lack access to a diverse range of AI models. While AI assistants like Claude Desktop are not designed for direct file editing (a feature planned for future releases, as Repo Prompt itself is a powerful file editor), the MCP server provides a unique solution.
The workflow involves setting up the prompt context within Repo Prompt. Once the context is prepared, you can copy it or move it to a chat interface. From there, you can open another tool, such as Cursor, which can then access the selected files and make targeted changes to your documentation.
By pasting the context, Cursor can immediately read the selected files from Repo Prompt, giving it the exact context it needs to work. This eliminates the need to manually re-select files or explain the context to the new tool. Cursor can invoke the read file tool to access related files from the open repositories—even across multiple projects, like web and native application code—and efficiently perform the required edits.
This demonstrates how you can creatively combine different tools by syncing context through the files selected in Repo Prompt. You can begin a task in one application and seamlessly continue in another, whether it's moving to an IDE like VS Code or another specialized tool. The MCP server ensures that these tools can read the provided context.
Essentially, the MCP server transforms Repo Prompt into a central hub, connecting the various tools developers use daily. It enables a fluid workflow where you can pick up work in one environment and continue in another, adapting to whatever process makes the most sense for the task at hand.
The result is that Cursor can make precise, targeted edits without any manual context explanation. This interoperability is a significant advantage. While the entire task could have been performed within a single tool, the ability to switch between applications is valuable, especially when dealing with things like different feature sets or API rate limits across services.
Setting Up the MCP Server in Just 3 Steps
Configuring the MCP server is straightforward. There are a few ways to get started.
1. Deep Link Integration A convenient button provides a deep link that jumps straight to Cursor, automatically configuring the integration.
2. Manual JSON Configuration Alternatively, you can open the MCP settings and find a JSON configuration file. For most applications, like Claude Desktop, you can simply copy this JSON into the tool's configuration file. In Claude Desktop, for example, you would navigate to settings, edit the configuration, and paste the JSON to complete the setup.
For other environments, the process is similar: paste the configuration and let the assistant handle the update. It can run the necessary commands to apply the new settings automatically.
3. Customizing Tool Visibility The MCP server settings also allow for tool customization. If you find that some tools are redundant or overlap with features in other applications, you can easily disable them. Disabling a tool hides it from the list of available options, preventing it from being advertised to connected applications. For instance, if you have more than 12+ tools and want to disable one, the count will drop accordingly. You can also turn off the server entirely to make it non-discoverable.
Advanced Use Case: The Advisor Model
One particularly helpful feature is the request plan, especially when you need access to specialized "advisor" models. By syncing the context (the selected files), you can have one AI assistant prompt another, more specialized one. For example, you can send the context to an advanced model to ask for advice on how to get unstuck or proceed with a complex task. This feature is continuously improving, with more personas and capabilities planned for the future.
